class_docstr: Token-based (input/output is tokens) encoder-decoder model (e.g. T5 Language model.)
code: def _validate_kv_channels(self, cfg):
      kv_channels = cfg.kv_channels
      if cfg.kv_channels is None:
          assert (
              cfg.hidden_size % cfg.num_attention_heads == 0
          ), "hidden_size must be divisible by num_attention_heads if kv_channels is None"
          kv_channels = cfg.hidden_size // cfg.num_attention_heads
  
      return kv_channels
  
imports: import torch
  from omegaconf import DictConfig
  from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import AdapterName, PromptEncoderAdapterConfig
  from nemo.collections.nlp.modules.common.megatron.hiddens import get_hiddens_module
  from nemo.collections.nlp.modules.common.megatron.language_model import Embedding
  from nemo.collections.nlp.modules.common.megatron.layer_type import LayerType
  from nemo.collections.nlp.modules.common.megatron.megatron_decoders import get_decoder_model
  from nemo.collections.nlp.modules.common.megatron.megatron_encoder_decoder import MegatronTransformerEncoderDecoderModule
  from nemo.collections.nlp.modules.common.megatron.megatron_encoders import get_encoder_model
  from nemo.collections.nlp.modules.common.megatron.module import MegatronModule
  from nemo.collections.nlp.modules.common.megatron.position_embedding import ALiBiRelativePositionEmbedding, KERPLERelativePositionEmbedding, T5RelativePositionEmbedding
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults, build_position_ids, init_method_normal, parallel_lm_logits, scaled_init_method_normal
  from nemo.collections.nlp.modules.common.megatron.vocab_parallel_cross_entropy import vocab_parallel_cross_entropy
  from nemo.core.classes.mixins import adapter_mixins
label_desc: Use image node_id 2 for calling the MegatronTokenLevelEncoderDecoderModule obj's underlying member method code with example usage: obj._validate_kv_channels(cfg) and returns: kv_channels